Drilling equipment three-dimensional track positioning method based on remote control

Abstract

The invention discloses a drilling equipment three-dimensional track positioning method based on remote control, and relates to the technical field of geological engineering, and the method comprises the following steps: obtaining target point cloud data of a drilling area through a laser scanner or a geological radar; preprocessing the obtained point cloud data, and calculating a normal vector of each measurement point by using a least square method; calculating a local curvature tensor of each measurement point based on the normal vector; performing trajectory optimization by adopting a non-Euclidean geometric model, constructing an objective function and introducing curvature constraint; after the optimized trajectory data is obtained, a three-dimensional finite element model is established, and the movement process of drilling equipment in different geological stratums is simulated; the three-dimensional track of the drilling equipment is monitored in real time through the remote control platform, and if it is detected that the track deviates from the preset path, a correction instruction is automatically sent. Dynamic changes in the underground environment can be effectively handled, the automation and intelligence level of drilling operation is improved, and the accuracy and stability of the drilling track are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of geological engineering, and in particular to a three-dimensional trajectory positioning method of a drilling device based on remote control. Background Art

[0002] In underground environments, especially in drilling operations in tunnels, mines and other fields, existing trajectory positioning methods mostly rely on traditional two-dimensional or three-dimensional modeling techniques based on Euclidean geometry. These methods predict the drilling path through simplified geometric models, and usually achieve good results in environments with relatively simple and stable geological conditions. However, when faced with highly complex and irregular underground geology, traditional trajectory optimization technology faces significant limitations and cannot effectively handle nonlinear and irregular geometric features in underground structures.

[0003] Existing technologies usually use models based on Euclidean geometry for path planning. These models cannot accurately reflect the complexity of the underground environment, such as stratum bending, rock fractures, or water flow effects, which often lead to deviations in the drilling path. Due to the lack of real-time response capabilities to geological changes, existing trajectory optimization methods cannot cope with dynamic changes in the underground environment. Therefore, traditional methods often cannot guarantee the accuracy and stability of the drilling path, and it is difficult to correct the drilling trajectory in real time, resulting in low operation efficiency and even possible equipment damage or operation failure.

[0004] In addition, traditional trajectory optimization methods are generally based on static path planning and lack effective integration with real-time status feedback of drilling equipment. Drilling equipment will be dynamically affected by the underground environment during operation. Therefore, static path planning methods often cannot cope with geological mutations and unexpected changes in actual situations. The trajectory of the equipment may deviate from the predetermined path, resulting in reduced operation accuracy and even affecting the overall operation progress and safety.

[0005] Therefore, a three-dimensional trajectory positioning method of drilling equipment based on remote control is proposed. Summary of the invention

[0006] The purpose of the present invention is to solve the problem that the existing trajectory positioning method cannot effectively handle nonlinear and irregular geometric features, resulting in deviations in the drilling path, and to propose a three-dimensional trajectory positioning method for drilling equipment based on remote control.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A three-dimensional trajectory positioning method for drilling equipment based on remote control, comprising the following steps: Step 1: Acquire target point cloud data in the drilling area by using a laser scanner or geological radar, wherein the point cloud data includes the three-dimensional coordinate information of each measuring point, and the point cloud data covers all or part of the spatial area of ​​the drilling path; Step 2: Preprocess the acquired point cloud data and use the least squares method to calculate the normal vector in the neighborhood of each measurement point. , where the normal vector Represents the local surface direction of the measurement point. The normal vector calculation is performed by fitting the point cloud within a certain radius around each measurement point to determine a neighborhood point set. The neighborhood point set is composed of the distance measurement point The plane equation of the neighborhood point set is solved based on the least squares method to obtain the local normal vector of the point. Step 3: Based on the result of normal vector calculation, the local curvature tensor of each measurement point is calculated by Laplace operator and second-order partial derivatives. , where the local curvature tensor represents the curvature of the measurement point and its neighborhood. The calculation process includes solving high-order partial derivatives in the neighborhood of the measurement point and calculating the principal curvature and ; Step 4: Use non-Euclidean geometry model to optimize the trajectory. Based on Riemann geometry theory, the optimization process of processing the path curvature includes: According to the local curvature tensor Construct the objective function of path optimization: ,in, is a dynamic geological factor, reflecting the physical characteristics of the geological layer where the i-th measurement point is located; and are weight coefficients, which adjust the relative influence of curvature and geological factors on path optimization respectively; is the influence coefficient of geological factors on path optimization; using the objective function, the drilling path is adjusted through the optimization algorithm and optimized along the trajectory with the minimum curvature; through iterative optimization, the accuracy and stability of the trajectory are guaranteed; Step 5: After obtaining the optimized trajectory data, a three-dimensional finite element model is established. The movement process of the drilling equipment in different geological layers is simulated by the finite element method, the mechanical response that the drilling equipment may encounter during geological changes is simulated, and the interaction between the equipment and the underground medium is analyzed. The stability of the drilling trajectory is further evaluated through the simulation results. The finite element simulation results are fed back to the drilling equipment to adjust the trajectory in real time to further improve the accuracy of the drilling operation. Step 6: The three-dimensional trajectory of the drilling equipment is monitored in real time through the remote control platform. If it is detected that the trajectory deviates from the predetermined path, a correction instruction is sent to the drilling equipment through the remote control system to automatically adjust the movement direction of the drilling equipment, thereby ensuring the accuracy and stability of the drilling trajectory.

[0008] Preferably, the calculation process of the normal vector includes: performing plane fitting on a set of points with a certain radius around each measuring point, and using the least squares method to solve the plane equation during the fitting process. , where a, b, c are the components of the plane normal vector, d is a constant term, and the normal vector is obtained , the normal vector Used to describe the local surface orientation at this measurement point.

[0009] Preferably, the local curvature tensor The calculation process includes: at each measurement point, the second-order partial derivatives of the neighboring points of the point are calculated to solve the local curvature tensor , and the principal curvature is obtained by eigenvalue decomposition and , and its curvature information reflects the curvature of the point in different directions.

[0010] Preferably, the trajectory optimization is based on the non-Euclidean geometry model and Riemann geometry theory described in step four, and adopts the shortest curvature path optimization algorithm to adjust the curvature of the drilling path and make the path as smooth as possible to adapt to complex underground geological layers and minimize the vibration and error of the drilling equipment.

[0011] Preferably, the trajectory optimization process is implemented through a curvature constrained optimization algorithm, and the specific steps are as follows: based on the point cloud data obtained by a laser scanner or a geological radar, a preliminary drilling path is generated using a straight line fitting method; the local curvature of each point on the path is calculated, and the path is adjusted through an optimization algorithm to minimize the curvature between adjacent points; the path is corrected through an iterative algorithm to minimize the curvature, and the path is adjusted in real time according to geological conditions to obtain the optimal path.

[0012] Preferably, the measurement point data is repeatedly collected by a laser scanner or a geological radar, and the data is filtered by a point cloud processing algorithm to remove outliers and noise data. The specific steps are as follows: use a laser scanner or a geological radar to scan multiple times to ensure that the data is comprehensive; apply the RANSAC algorithm to remove abnormal data, and use DBSCAN clustering to identify and delete error points; smooth the data through weighted averaging, and fill in the missing areas by interpolation to ensure that the data is evenly distributed.

[0013] Preferably, the finite element simulation in step five performs dynamic modeling of the drilling equipment through ABAQUS or ANSYS software, combines the physical properties of the underground medium, simulates the mechanical response during the drilling process, and optimizes the trajectory path according to the simulation results. The specific steps are as follows: use ABAQUS or ANSYS for three-dimensional modeling, and ensure calculation accuracy through meshing; simulate the interaction between the drilling equipment and the underground medium, and calculate the resistance, vibration and dynamic load of the equipment; adjust the drilling path according to the simulation results to avoid the equipment from entering high-pressure or soft geological layers, and ensure stability and accuracy.

[0014] Preferably, the drilling equipment further comprises an intelligent feedback module, which is linked with the sensor network of the drilling equipment, and can monitor the operating status of the drilling equipment in real time, and issue an alarm or make fine adjustments when the trajectory deviates.

[0015] Preferably, the three-dimensional trajectory positioning method combines a B-spline surface or a NURBS surface model to model underground geological data, and uses it to construct a geometric reference for the drilling path to improve the accuracy of the trajectory.

[0016] The present invention has the following beneficial effects: In the present invention, by combining technologies such as laser scanning, geological radar, non-Euclidean geometric model and finite element analysis, the positioning accuracy of drilling equipment in complex underground environments is significantly improved. The application of laser scanning and geological radar makes the acquired three-dimensional point cloud data more comprehensive and accurate, and can effectively reflect the complexity of underground geological structures. By calculating the normal vector and local curvature tensor by the least squares method, the local surface characteristics of the measuring point can be accurately described, providing a reliable data basis for subsequent trajectory optimization. In addition, the introduction of non-Euclidean geometric model makes the path optimization process more flexible, can adapt to the drilling needs under different geological conditions, and reduces the vibration and error of the drilling equipment.

[0017] In the present invention, the automation and intelligence level of drilling operations are improved through remote control technology. This method can not only detect the trajectory deviation of the drilling equipment in real time, but also automatically generate correction instructions to ensure that the equipment always operates along the predetermined path. This dynamic adjustment mechanism significantly improves the safety and efficiency of drilling operations and reduces the risk of equipment damage and operation failure caused by trajectory deviation. At the same time, the stability of the drilling path is evaluated through finite element analysis, which further optimizes the accuracy of the drilling operation and ensures the stable operation of the equipment under different geological conditions. Overall, the present invention provides an efficient and reliable solution for drilling operations and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1The present invention proposes a three-dimensional trajectory positioning method for drilling equipment based on remote control. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, the present invention proposes a three-dimensional trajectory positioning method for drilling equipment based on remote control.

[0021] Step 1: Obtaining measurement point data: This step aims to obtain 3D point cloud data of the drilling area through a laser scanner (LiDAR) or geological radar (GPR) to provide accurate measurement point coordinate information. This data will serve as the basis for subsequent calculation of the drilling trajectory. The specific process is as follows: 1. Equipment selection and layout: Laser scanner (LiDAR): Laser scanners measure the distance of target objects by emitting laser beams and receiving reflected laser signals, thereby obtaining the three-dimensional coordinates of the measurement points. Geological radar (GPR): Geological radar is used to detect underground structures and their physical properties by emitting high-frequency electromagnetic waves and receiving wave signals reflected from different underground media. Compared with laser scanners, GPR is particularly suitable for obtaining information about underground geological layers, especially when there are irregular underground media or obstacles, it can provide additional measurement data. By scanning the underground structure, detailed information about the geological layers below and around the drilling path can be obtained.

[0022] 2. Measurement area selection and coverage: Before measuring, the specific scope of the area where the borehole path is located and its surrounding areas must be determined. The measurement area should cover all or part of the space of the borehole path to ensure that the data is sufficient and comprehensive. The measurement data needs to include spatial information of the geological layers around the borehole to provide an accurate reference for subsequent trajectory calculations. In view of the complexity of the borehole path, the measurement equipment needs to be deployed from multiple angles to ensure that the point cloud data of the entire borehole area can be fully covered. The equipment should be deployed in appropriate locations to maximize the coverage of the measurement area, especially in the presence of high geological complexity or obstacles, the integrity and accuracy of the data need to be guaranteed.

[0023] 3. Data collection: Laser scanning data collection: The laser scanner is used to fully scan the measurement area and obtain the point cloud data of the target area. The laser scanner calculates the distance between each measurement point and the scanner by quickly emitting a laser beam and receiving the returned reflected signal. Through this process, the spatial position (i.e., X, Y, and Z coordinates) of each measurement point is accurately recorded. Geological radar data collection: In the drilling area, high-frequency electromagnetic waves are emitted by the geological radar equipment and the reflected waves of underground materials are received. Based on the propagation time of the reflected signal, the geological radar can calculate the depth and morphology of different underground layers and structures. This process can generate two-dimensional or three-dimensional images of underground structures and provide information on the geological distribution and obstacles below the drilling path. By collecting data multiple times, comprehensive underground data can be obtained at different depths and directions to further improve the prediction of the drilling path.

[0024] 4. Point cloud data processing and storage: Data processing: The acquired raw point cloud data usually contains noise and outliers, especially in complex environments, where sensors may be affected by external factors. To ensure the accuracy of the data, the point cloud data needs to be preprocessed. Remove erroneous or inaccurate data points that occur during the measurement process. Data storage: The processed point cloud data will be stored in a standard format (such as PLY, XYZ, LAS, etc.) and provide input for subsequent steps. These data not only contain the three-dimensional coordinates (X, Y, Z) of the measurement points, but may also include other relevant parameters of each point (such as reflection intensity, point cloud density, sampling angle, etc.), which help to further optimize the accuracy of trajectory calculation and path planning.

[0025] Step 2: Normal vector calculation: In this step, based on the acquired point cloud data, the neighborhood of each measurement point is processed to calculate the local normal vector of the measurement point. The normal vector can effectively describe the local surface direction of the measurement point, thus providing an important reference for subsequent local curvature calculation, trajectory optimization, and drilling path stability analysis. The specific steps are as follows: 1. First, the neighborhood range of each measurement point needs to be determined. , by setting an appropriate radius r to determine the neighborhood of the point. The point set in the neighborhood is determined by the distance measurement point The size and distribution of the neighborhood point set will affect the calculation accuracy of the normal vector. Therefore, the selection of the radius r needs to be adjusted according to the actual environment and the density of the point cloud data to ensure that the neighborhood point set is sufficiently representative.

[0026] After obtaining the neighborhood point set, these points are preprocessed to remove potential noise points or outliers. For example, a distance threshold is used to remove outliers, or statistical methods are used to remove points that deviate from the main trend to ensure that the remaining point set reflects the geometric characteristics of the local surface as realistically as possible.

[0027] 2. To calculate the local normal vector of the measurement point, it is necessary to perform plane fitting on the selected neighborhood point set. Specifically, the least squares method is used to fit a plane model, which is used to represent the local surface of the neighborhood point set. The least squares fitting process is as follows: Assuming measurement point The neighborhood point set of , where each point All have known 3D coordinates , where the subscript j represents an index from 1 to n.

[0028] This plane is expressed in the form of a plane equation: , where a, b, c are the components of the plane normal vector and d is a constant term.

[0029] The goal of the least squares method is to minimize the distance from the fitted plane to the neighborhood point set by adjusting the values ​​of a, b, c, and d. Specifically, the least squares method minimizes the sum of the perpendicular distances from each point to the fitted plane:

[0030] Through this optimization process, the best fitting parameters of the plane equation can be obtained, that is, the components of the plane normal vector . This normal vector represents the measured point The local surface orientation at the location.

[0031] 3. Calculated normal vector is the local normal vector of the measured point, which describes the orientation of the surface in the neighborhood of the point. During point cloud data processing, you may encounter situations where the normal vector direction is inconsistent or reversed. For example, in different parts of the point cloud, the direction of the normal vector may be opposite to the expected geological structure direction, resulting in inconsistent calculation results.

[0032] To ensure the correctness of the normal vector, calibration is performed. This includes: adjusting the direction of the normal vector by comparing it with known geological structures or other measurement data. Using the direction constraint of the surface normal vector, for example, assuming that the local surface where the measurement point is located should face the direction of movement of the drilling equipment, thereby adjusting the direction of the normal vector to match the actual situation.

[0033] This step reduces the curvature of the preliminary path, improves the accuracy of the three-dimensional trajectory, and avoids excessive deviation of the path through the preliminary generated trajectory and curvature optimization.

[0034] Step 3: Local curvature tensor calculation: In this step, based on the result of the normal vector calculation in the previous step, the local curvature tensor of each measurement point is calculated through the Laplace operator and the second-order partial derivative. , the local curvature tensor describes the curvature of the measurement point and its neighborhood. The local curvature tensor contains the principal curvatures and , which represent the maximum and minimum curvatures at the measuring point respectively. The two principal curvatures reflect the curvature of the local geological structure in two orthogonal directions.

[0035] 1. In order to extract curvature information from point cloud data, it is first necessary to operate the local area of ​​the measurement point through the Laplace operator. The curvature characteristics of the calculation function at a certain point. In point cloud data, the Laplace operator can reveal the curvature of the local surface.

[0036] The specific steps are as follows: For each measuring point The neighborhood of the surface is smoothed to remove the influence of noise and ensure the accuracy of the data. The mathematical model of the neighborhood points is constructed by interpolating the points in the neighborhood, usually using interpolation methods such as polynomials or B-splines. The second-order derivative of each point in the neighborhood is calculated to obtain the local curvature information of the point. The role of the Laplace operator in this method is to extract the geometric features of the local surface into a mathematical expression and further provide the required data support for the curvature calculation.

[0037] 2. Local curvature tensor The calculation of depends on the second-order partial derivatives of the neighborhood of the measurement point. Specifically, we first need to calculate the measurement point The surrounding point cloud data is subjected to high-order differential calculation to obtain relevant information about the surface curvature. The calculation process is as follows: Second-order partial derivative calculation: At each measurement point At , calculate the second-order partial derivatives of the point in two orthogonal directions. That is, calculate , where z represents the height of the measurement point, and x and y are the plane coordinate axes. Through these partial derivatives, the second-order rate of change of the surface at that point is obtained. Construct the local curvature tensor: Based on the above partial derivatives, construct the local curvature tensor , the tensor can be expressed as: The local curvature tensor contains the second-order curvature characteristics of the surface where the measurement point is located. The principal curvature can be obtained by eigenvalue decomposition. and .

[0038] 3. Obtain the principal curvature by performing eigenvalue decomposition on the local curvature tensor and , these two values ​​represent the maximum and minimum curvature of the local geological layer in two orthogonal directions. The process of calculating the principal curvature is as follows: For the local curvature tensor Perform eigenvalue decomposition and obtain its eigenvalue The two eigenvalues ​​correspond to the curvature of the local surface in two orthogonal directions. If the principal curvature is large, it means that the curvature of the point area is high, and the drilling path may need to be adjusted in this area to avoid deviation of the path or excessive load on the equipment.

[0039] This step helps reduce path bending, thereby improving the accuracy of the drilling path in complex underground environments, avoiding unnecessary trajectory distortion, and ensuring a smoother and more stable three-dimensional trajectory.

[0040] Step 4: Non-Euclidean geometry optimization: In this step, the non-Euclidean geometry model is used to optimize the drilling path. By introducing Riemann geometry theory, in three-dimensional space, based on curvature constraints, the path is adjusted through an optimization algorithm to ensure that the drilling path follows the shortest curvature path.

[0041] 1. In the process of path optimization, dynamic geological factors and local curvature information are introduced to construct the optimization objective function. The purpose of the objective function is to optimize the smoothness of the path by minimizing the curvature of the drilling path, while considering the adaptability of the path under different geological conditions. The objective function is defined as follows: ,in, is a dynamic geological factor, reflecting the physical characteristics of the geological layer where the i-th measurement point is located; and are weight coefficients, which adjust the relative influence of curvature and geological factors on path optimization respectively; is the influence coefficient of geological factors on path optimization; 2. During the path optimization process, limit the curvature of the drilling path to avoid excessive curvature of the path. Set curvature constraints to ensure that the local curvature of the path does not exceed the set maximum value. , the specific form is:

[0042] 3. The local curvature information obtained from step 3 is used as the input for path optimization. The local curvature value of each measurement point is and This data will be used as the main basis for path optimization. These data help determine the best bending direction of the drilling path at each point.

[0043] Path adjustment algorithm: based on local curvature information , combined with dynamic geological factors , guiding the path optimization process. The drilling path is adjusted through numerical optimization methods (such as gradient descent method, Newton method, etc.). In each iteration, the algorithm adjusts the path according to the curvature distribution of the current path, so that the curvature of the path is gradually reduced and excessive curvature is avoided in unsuitable geological areas. The specific path adjustment formula is as follows:

[0044] in, are the coordinates of the path at step t, is the learning rate, which controls the step size of each path adjustment. is the gradient of the objective function at the current path point, indicating the direction of the path adjustment at the current position. Through this formula, dynamic geological factors change in real time as the drilling process progresses, such as the hardness, humidity, and pressure of the geological layer. The optimization algorithm updates the path in each iteration and gradually converges to the optimal path. The optimization process stops when one of the following conditions is met: Objective Function convergence; The path adjustment amplitude is less than the set threshold; The maximum number of iterations has been reached.

[0045] This step optimizes the drilling path through a non-Euclidean geometry model, combines Riemann geometry theory, and combines curvature constraints with objective function optimization to minimize the curvature of the drilling path. Through an optimization algorithm based on local curvature information, the drilling path is adjusted to adapt to different geological layer structures, ensuring the shortest curvature of the path, and ultimately improving drilling accuracy and operation stability.

[0046] Step 5: Finite Element Analysis and 3D Modeling: In this step, the drilling path is further simulated and optimized through finite element analysis (FEA) to ensure the stability and accuracy of the drilling equipment under different geological conditions. By combining the dynamic characteristics of the drilling equipment and the physical properties of the underground geological medium, a 3D finite element model is established to simulate the interaction between the motion trajectory of the drilling equipment and the underground geological layer. The core goal of this process is to analyze and optimize the stability of the drilling path, thereby improving the efficiency and safety of the drilling operation.

[0047] 1. Finite element analysis (FEA) is a numerical calculation method widely used in solving structural and physical problems. Finite element analysis is used to simulate the interaction between drilling equipment and underground geological layers, and optimize the drilling path by analyzing the behavior of the equipment in the underground environment. Including: First, through the data of the actual drilling operation site, the various physical parameters of the underground geological layer (such as density, elastic modulus, Poisson's ratio, etc.) and the dynamic characteristics of the drilling equipment (such as mass, moment of inertia, vibration characteristics, etc.) are obtained. These parameters constitute the basic input of the finite element model. The specific construction steps are as follows: Modeling of geological media: The modeling of geological layers depends on the geological data obtained from on-site drilling, including physical properties such as thickness, hardness, shear modulus, and density of the formation. Modeling of drilling equipment: The dynamic characteristics of drilling equipment include its structural stiffness, vibration frequency, movement mode, etc. Construction of contact model: The interaction between drilling equipment and underground geological layers is described by contact model.

[0048] Finite element analysis process: Dynamic simulation is performed through the finite element model to simulate the actual movement process of the drilling equipment in the underground medium. The main task of finite element analysis is to calculate the interaction force between the drilling equipment and the geological medium, including cutting force, friction force, pressure distribution, etc. The main steps involved in the analysis process are as follows: First, set the boundary conditions in the simulation process to ensure that the force and motion state of the drilling equipment in actual operation can be accurately simulated. The setting of boundary conditions includes: The initial state of the equipment: including the position, speed, acceleration, etc. of the equipment. Boundary conditions of geological media: Set the physical constraints of the underground geological layer, especially the influence of physical parameters such as the elastic modulus and shear modulus of the medium on the drilling path. Contact force setting: The contact force model between the drilling equipment and the geological medium includes friction, normal force and cutting force.

[0049] In dynamic simulation, the finite element model considers the dynamic response of the drilling equipment when it contacts the geological medium, including the motion trajectory and vibration response of the drilling equipment. By solving the mechanical equations, the stress conditions of the drilling equipment at different stages can be obtained, and the stability of the drilling path can be further analyzed. The motion state of the drilling equipment can be analyzed by solving the dynamic variables such as displacement, velocity, acceleration, etc. of the equipment at each moment. Dynamic analysis can reveal the vibration and instability that may occur in the equipment during the drilling process, such as path deviation or equipment damage. Mechanical analysis performs force analysis on each point on the drilling path and calculates the force distribution of the drilling equipment under different geological conditions, especially the cutting force and friction when contacting the geological layer. The drilling path can be optimized to ensure that the equipment maintains good stability in the underground environment.

[0050] Based on the results of finite element analysis, the stability and accuracy of the drilling path are evaluated. Specific evaluation indicators include: Path stability: Analyze the deviation and error of the drilling path, evaluate the movement stability of the equipment under different geological conditions, and ensure that the path will not bend sharply or become unstable. Vibration response: By analyzing the vibration mode of the drilling equipment, evaluate whether there is resonance or excessive vibration, which affects the drilling accuracy. Force distribution: Analyze the force distribution during the contact process of different geological layers, especially the changes in cutting force during drilling, so as to determine whether the drilling equipment can cut smoothly and maintain accuracy.

[0051] 3. The results obtained through finite element analysis will provide guidance for the optimization of subsequent steps. The drilling path is corrected according to the analysis results to ensure that the equipment can remain stable under different geological conditions and optimize the drilling trajectory. Specific feedback and correction measures include: Path correction: Based on the stability assessment of finite element analysis, the drilling path is corrected to avoid excessive bending or instability of the equipment in the underground environment. Dynamic characteristic adjustment: If the finite element analysis finds that the vibration of the drilling equipment is too large or resonance occurs, the stability of the equipment can be improved by adjusting the dynamic characteristics of the equipment, such as increasing damping, changing the movement mode, etc. Contact force adjustment: During the analysis process, if it is found that the contact force between the drilling equipment and the geological medium is unevenly distributed, it may cause excessive wear of the equipment or path deviation. Further optimize the contact force model and adjust the movement mode of the drilling equipment.

[0052] This step uses a real-time path correction mechanism to ensure that the path always remains on the predetermined trajectory during the drilling process, thereby improving positioning accuracy, avoiding path deviation or unstable equipment operation, and further improving the real-time response capability and accuracy of the trajectory.

[0053] Step 6: Remote control and trajectory correction: In this step, the remote control platform is used to monitor the movement trajectory of the drilling equipment in real time to ensure that the equipment can accurately perform drilling operations along the predetermined path in the underground environment. The real-time position and trajectory of the drilling equipment are detected by the remote control system. If the equipment deviates from the predetermined trajectory, the system can automatically issue correction instructions to adjust the movement direction and position of the equipment to ensure the accuracy and stability of the drilling operation. This process not only improves the accuracy of the drilling operation, but also significantly enhances the automation and intelligence level of the operation.

[0054] 1. Positioning sensor: Use high-precision global positioning system (GPS) or inertial measurement unit (IMU) sensors to track the position of drilling equipment in real time. Tilt sensor and attitude detection: Including the tilt angle and rotation angle of the drill head. Real-time data transmission: The data collected by the sensor is transmitted to the remote control platform in real time through wireless communication technology (such as 4G / 5G, Wi-Fi or dedicated communication links).

[0055] 2. Trajectory deviation detection is mainly carried out in the following ways: Comparison between the planned trajectory and the actual trajectory: The planned trajectory is the ideal path set before the drilling operation based on geological data and the operation plan. The remote monitoring system receives the actual trajectory data of the equipment in real time and compares it with the planned trajectory. If the deviation of the equipment exceeds the preset threshold (such as deviation angle, position deviation), the trajectory is considered to have deviated. Trajectory error calculation: The system calculates the magnitude of the trajectory error based on the motion state and actual path of the equipment. The error value may be a distance error or an angle error. The system will determine whether trajectory correction is required based on the error magnitude. Multi-sensor fusion: The accuracy of trajectory detection is improved by fusing data from different sensors (such as GPS, IMU, tilt sensor, etc.). The multi-sensor fusion algorithm can effectively reduce the error of a single sensor and provide more stable and accurate trajectory data.

[0056] When the drilling equipment's trajectory is detected to deviate from the predetermined path, the remote control platform will automatically generate correction instructions. These correction instructions will be sent to the drilling equipment to guide the equipment to adjust its movement direction.

[0057] Trajectory correction algorithm: According to the type of trajectory deviation (such as position deviation, angle deviation, etc.), the system automatically adjusts the movement path of the drilling equipment by calculating the correction amount. The correction process includes adjusting the direction, speed and feed rate of the drill bit to ensure that the equipment returns to the predetermined trajectory. Motion control system: The correction command is sent to the motion control system of the drilling equipment through wireless communication, and the control system adjusts the motion parameters of the equipment according to the command. These parameters include the forward direction of the equipment, steering angle, drilling speed, etc., to ensure that the drilling equipment can accurately correct the trajectory. Dynamic correction feedback mechanism: During the execution of the correction command, the system will continuously monitor the position changes of the equipment to ensure real-time feedback and adjustment of the correction process. If the trajectory deviation is not completely eliminated after the first correction, the system will continue to make dynamic adjustments until the equipment returns completely to the predetermined trajectory.

[0058] 3. After the trajectory correction operation is completed, the correction effect needs to be evaluated to ensure that the drilling equipment has returned to the predetermined trajectory and the operating accuracy has been restored. The evaluation methods include: Accuracy comparison: Compare the corrected trajectory with the predetermined trajectory to evaluate the degree of deviation of the equipment from the trajectory and the accuracy recovery after correction. Correction response time: Evaluate the speed at which the system responds to the correction instruction to ensure that the correction process is fast and timely and does not affect the overall drilling operation progress. Operation efficiency: Evaluate the impact of the correction operation on the drilling operation efficiency to ensure the continuity and stability of the drilling operation during the correction process.

[0059] Step 6: Precise control and real-time adjustment of the drilling equipment are achieved through remote control and trajectory correction mechanism. When the equipment deviates from the predetermined trajectory, the remote monitoring platform automatically issues correction instructions to adjust the movement direction and position of the equipment to ensure the accuracy and stability of the drilling operation.

[0060] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A three-dimensional trajectory positioning method for drilling equipment based on remote control, characterized in that: The following steps are involved: Step 1: Acquire target point cloud data in the drilling area by using a laser scanner or geological radar, wherein the point cloud data includes the three-dimensional coordinate information of each measuring point, and the point cloud data covers all or part of the spatial area of ​​the drilling path; Step 2: Preprocess the acquired point cloud data and use the least squares method to calculate the normal vector in the neighborhood of each measurement point. , where the normal vector Represents the local surface direction of the measurement point. The normal vector calculation is performed by fitting the point cloud within a certain radius around each measurement point to determine a neighborhood point set. The neighborhood point set is composed of the distance measurement point The plane equation of the neighborhood point set is solved based on the least squares method to obtain the local normal vector of the point. Step 3: Based on the result of normal vector calculation, the local curvature tensor of each measurement point is calculated by Laplace operator and second-order partial derivatives. , where the local curvature tensor represents the curvature of the measurement point and its neighborhood. The calculation process includes solving high-order partial derivatives in the neighborhood of the measurement point and calculating the principal curvature and ; Step 4: Use non-Euclidean geometry model to optimize the trajectory. Based on Riemann geometry theory, the optimization process of processing the path curvature includes: According to the local curvature tensor Construct the objective function of path optimization: ,in, is a dynamic geological factor, reflecting the physical characteristics of the geological layer where the i-th measurement point is located; and are weight coefficients, which adjust the relative influence of curvature and geological factors on path optimization respectively; is the influence coefficient of geological factors on path optimization; using the objective function, the drilling path is adjusted through the optimization algorithm and optimized along the trajectory with the minimum curvature; through iterative optimization, the accuracy and stability of the trajectory are guaranteed; Step 5: After obtaining the optimized trajectory data, a three-dimensional finite element model is established. The movement process of the drilling equipment in different geological layers is simulated by the finite element method, the mechanical response that the drilling equipment may encounter during geological changes is simulated, and the interaction between the equipment and the underground medium is analyzed. The stability of the drilling trajectory is further evaluated through the simulation results. The finite element simulation results are fed back to the drilling equipment to adjust the trajectory in real time to further improve the accuracy of the drilling operation. Step 6: The three-dimensional trajectory of the drilling equipment is monitored in real time through the remote control platform. If it is detected that the trajectory deviates from the predetermined path, a correction instruction is sent to the drilling equipment through the remote control system to automatically adjust the movement direction of the drilling equipment, thereby ensuring the accuracy and stability of the drilling trajectory.

2. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The calculation process of the normal vector includes: performing plane fitting on a set of points with a certain radius around each measuring point, and using the least squares method to solve the plane equation during the fitting process. , where a, b, c are the components of the plane normal vector, d is a constant term, and the normal vector is obtained , the normal vector Used to describe the local surface orientation at this measurement point.

3. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The local curvature tensor The calculation process includes: at each measurement point, the second-order partial derivatives of the neighboring points of the point are calculated to solve the local curvature tensor , and the principal curvature is obtained by eigenvalue decomposition and , and its curvature information reflects the curvature of the point in different directions.

4. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The trajectory optimization is based on the non-Euclidean geometry model and Riemann geometry theory described in step 4, and adopts the shortest curvature path optimization algorithm to adjust the curvature of the drilling path and make the path as smooth as possible to adapt to the complex underground geological layers and minimize the vibration and error of the drilling equipment.

5. The method for three-dimensional trajectory positioning of drilling equipment based on remote control according to claim 1, characterized in that: The trajectory optimization process is implemented through a curvature constrained optimization algorithm, and the specific steps are as follows: based on the point cloud data obtained by a laser scanner or a geological radar, a preliminary drilling path is generated using a straight line fitting method; the local curvature of each point on the path is calculated, and the path is adjusted through an optimization algorithm to minimize the curvature between adjacent points; the path is corrected through an iterative algorithm to minimize the curvature, and the path is adjusted in real time according to geological conditions to obtain the optimal path.

6. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The measurement point data is repeatedly collected by laser scanner or geological radar, and the data is filtered by point cloud processing algorithm to remove outliers and noise data. The specific steps are as follows: use laser scanner or geological radar to scan multiple times to ensure comprehensive data; apply RANSAC algorithm to remove abnormal data, use DBSCAN clustering to identify and delete error points; smooth data by weighted average, and fill in missing areas by interpolation to ensure uniform data distribution.

7. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The finite element simulation in step five uses ABAQUS or ANSYS software to perform dynamic modeling of the drilling equipment, combines the physical properties of the underground medium, simulates the mechanical response during the drilling process, and optimizes the trajectory path based on the simulation results. The specific steps are as follows: use ABAQUS or ANSYS for three-dimensional modeling, and ensure calculation accuracy through meshing; simulate the interaction between the drilling equipment and the underground medium, and calculate the resistance, vibration and dynamic load of the equipment; adjust the drilling path according to the simulation results to prevent the equipment from entering high-pressure or soft geological layers, and ensure stability and accuracy.

8. The method for three-dimensional trajectory positioning of drilling equipment based on remote control according to claim 1, characterized in that: The drilling equipment further includes an intelligent feedback module, which is linked to the sensor network of the drilling equipment and can monitor the operating status of the drilling equipment in real time, and issue an alarm or make fine adjustments when the trajectory deviates.

9. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The three-dimensional trajectory positioning method combines B-spline surface or NURBS surface model to model underground geological data, and is used to construct a geometric reference of the drilling path to improve the accuracy of the trajectory.

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